code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_required(labels)
      c_f.ref_not_supported(embeddings, labels, ref_emb, ref_labels)
      dtype, device = embeddings.dtype, embeddings.device
      miner_weights = lmu.convert_to_weights(
          indices_tuple, labels, dtype=dtype
      )
      N = labels.size(0)
      a1_idx, p_idx, a2_idx, n_idx = lmu.get_all_pairs_indices(labels)
      I_pos = torch.zeros(N, N, dtype=dtype, device=device)
      I_neg = torch.zeros(N, N, dtype=dtype, device=device)
      I_pos[a1_idx, p_idx] = 1
      I_neg[a2_idx, n_idx] = 1
      N_pos = torch.sum(I_pos, dim=1)
      safe_N = N_pos > 0
      if torch.sum(safe_N) == 0:
          return self.zero_losses()
      dist_mat = self.distance(embeddings)
  
      histogram_max = 2**self.distance.power
      histogram_delta = histogram_max / self.num_bins
      mid_points = torch.linspace(
          0.0,
          histogram_max,
          steps=self.num_edges,
          device=device,
          dtype=dtype,
      ).view(-1, 1, 1)
      pulse = torch.nn.functional.relu(
          1 - torch.abs(dist_mat - mid_points) / histogram_delta
      )
      pos_hist = torch.t(torch.sum(pulse * I_pos, dim=2))
      neg_hist = torch.t(torch.sum(pulse * I_neg, dim=2))
  
      total_pos_hist = torch.cumsum(pos_hist, dim=1)
      total_hist = torch.cumsum(pos_hist + neg_hist, dim=1)
  
      h_pos_product = pos_hist * total_pos_hist
      safe_H = (h_pos_product > 0) & (total_hist > 0)
      if torch.sum(safe_H) > 0:
          FastAP = torch.zeros_like(pos_hist, device=device)
          FastAP[safe_H] = h_pos_product[safe_H] / total_hist[safe_H]
          FastAP = torch.sum(FastAP, dim=1)
          FastAP = FastAP[safe_N] / N_pos[safe_N]
          FastAP = (1 - FastAP) * miner_weights[safe_N]
          return {
              "loss": {
                  "losses": FastAP,
                  "indices": torch.where(safe_N)[0],
                  "reduction_type": "element",
              }
          }
      return self.zero_losses()
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/fast_ap_loss.py
imports: import torch
  from ..distances import LpDistance
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 2 for calling the FastAPLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: self, self, dict